Literature DB >> 23852554

A novel low-power-implantable epileptic seizure-onset detector.

M T Salam, M Sawan.   

Abstract

A novel implantable low-power integrated circuit is proposed for real-time epileptic seizure detection. The presented chip is part of an epilepsy prosthesis device that triggers focal treatment to disrupt seizure progression. The proposed chip integrates a front-end preamplifier, voltage-level detectors, digital demodulators, and a high-frequency detector. The preamplifier uses a new chopper stabilizer topology that reduces instrumentation low-frequency and ripple noises by modulating the signal in the analog domain and demodulating it in the digital domain. Moreover, each voltage-level detector consists of an ultra-low-power comparator with an adjustable threshold voltage. The digitally integrated high-frequency detector is tunable to recognize the high-frequency activities for the unique detection of seizure patterns specific to each patient. The digitally controlled circuits perform accurate seizure detection. A mathematical model of the proposed seizure detection algorithm was validated in Matlab and circuits were implemented in a 2 mm(2) chip using the CMOS 0.18- μm process. The proposed detector was tested by using intracerebral electroencephalography (icEEG) recordings from seven patients with drug-resistant epilepsy. The seizure signals were assessed by the proposed detector and the average seizure detection delay was 13.5 s, well before the onset of clinical manifestations. The measured total power consumption of the detector is 51 μW.

Entities:  

Year:  2011        PMID: 23852554     DOI: 10.1109/TBCAS.2011.2157153

Source DB:  PubMed          Journal:  IEEE Trans Biomed Circuits Syst        ISSN: 1932-4545            Impact factor:   3.833


  4 in total

1.  Online analysis of local field potentials for seizure detection in freely moving rats.

Authors:  Meysam Zare; Milad Nazari; Amir Shojaei; Mohammad Reza Raoufy; Javad Mirnajafi-Zadeh
Journal:  Iran J Basic Med Sci       Date:  2020-02       Impact factor: 2.699

2.  A High Performance Delta-Sigma Modulator for Neurosensing.

Authors:  Jian Xu; Menglian Zhao; Xiaobo Wu; Md Kafiul Islam; Zhi Yang
Journal:  Sensors (Basel)       Date:  2015-08-07       Impact factor: 3.576

3.  A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection.

Authors:  Farrokh Manzouri; Simon Heller; Matthias Dümpelmann; Peter Woias; Andreas Schulze-Bonhage
Journal:  Front Syst Neurosci       Date:  2018-09-20

4.  Online Prediction of Lead Seizures from iEEG Data.

Authors:  Hsiang-Han Chen; Han-Tai Shiao; Vladimir Cherkassky
Journal:  Brain Sci       Date:  2021-11-24
  4 in total

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